Searching, sorting, and displaying video clips and sound files by relevance

ABSTRACT

A documents database has a plurality of documents, including but not limited to text files, video clips and sound files. Each document is associated with at least one category of a plurality of categories in a categories database, and each category has at least one keyword. A search request having at least one search term is received from a user, and a categories database is searched for categories having a keyword corresponding to the user search term to identify first level categories. The other keywords from the identified first level categories are retrieved and the documents database is searched for documents having a user search term or a retrieved keyword. The identified documents are then ranked and presented to the user. Other search expansion techniques, and display techniques, are also discussed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.12/138,770, filed Jun. 13, 2008, the disclosure of which is incorporatedherein by reference in its entirety.

FIELD

The present invention relates to document search engines and, moreparticularly, to searching, ranking, and displaying documents, includingbut not limited to text files, video clips and sound files.

BACKGROUND OF THE INVENTION

Current search engines search for documents which have one or more ofthe search terms specified by the user initiating the search. Thesesearch engines, however, miss documents which do not contain thosespecific terms. Current search engines also sort or rank the identifieddocuments by, for example, the number of times a search term is used inthe documents, monetary consideration paid by a person or entity wishingfor a particular document to be ranked higher, etc. This can result in adocument having a higher ranking or a more prominent display positionsimply because someone is exploiting the system by using a searchterm(s) numerous times in the document, or because there is someonewilling to pay to have the document ranked more highly. Current searchengines also display the identified and ranked documents in aone-dimensional manner; that is, a single column which lists the highestranked document first, the second-highest ranked document second, etc.This requires the user to review numerous documents to try to find thosethat are truly relevant.

SUMMARY

One method provides for searching for documents in a documents database.The documents database has a plurality of documents, each document isassociated with at least one category of a plurality of categories in acategories database, and each category has at least one keyword. Thedocuments include, but are not limited to, text files, video clips andsound files. A search request having at least one search term isreceived from a user, and a categories database is searched forcategories having a keyword corresponding to the user search term toidentify first level categories. The other keywords from the identifiedfirst level categories are retrieved and the documents database issearched for documents having a user search term or a retrieved keyword.The identified documents are then ranked and presented to the user.

Another method provides for ranking documents produced in response to asearch request to a search engine. The search request has search terms.The documents are scored in accordance with a predetermined scoringprocess, the scores of the documents are then altered based upon atleast one of: a normalized average rating for the document based uponratings of the document by users who have accessed the document, thedocument type, the source of the document, the number of times thedocument has been accessed, the primary person featured in the document,the title of the primary person featured in the document, or thepolitical office of the primary person featured in the document. Thedocuments are then ranked in accordance with the results of the alteredscoring. The documents include, but are not limited to, text files,video clips and sound files.

Another method provides for ranking documents produced by a search. Eachsearch result document has an initial ranking. Event condition criteriaare then applied to the documents to change the rankings to providere-ranked search result documents. The documents include, but are notlimited to, text files, video clips and sound files.

Another method provides for ranking categories. Each category has atleast one keyword and a plurality of associated documents. A searchengine identifies relevant categories based upon a comparison ofcategory keywords with search terms in a search request provided to thesearch engine. A score is assigned to each category based upon thenumber of searches made regarding that category, the number of definedcategories, the number of searches made for each category, the weight ofthe relationship between that category and all other categories, theweighted relationship between that category and another category, theweighted number of searches made against a related category, the numberof documents related to that category, and the number of documentsrelated to a category related to that category. The categories are thenranked based upon the assigned scores. The documents include, but arenot limited to, text files, video clips and sound files.

Another method provides for ranking persons. Each person is associatedwith at least one document. A search engine identifies relevantdocuments based upon a search request for a person. A score is assignedto each person based upon the broadness of impact rank of the person,the number of searches made regarding that person, the number ofsearches made regarding each of the persons, the proximity of anelection involving that person, the proximity of an election involvingthat person, the proximity of elections for the time before the date ofthe election involving that person, the proximity of elections for thetime after the date of the election involving that person. The personsare then ranked based upon the assigned scores. The documents include,but are not limited to, text files, video clips and sound files.

Another method provides a display which indicates the relevance ofdocuments to two different factors, such as categories and persons.Categories to which each document is related are identified and thepersons to which each document is related are identified. The categoriesand the persons are ranked. An array having a plurality of cells isestablished, a first cell indicates documents which are related to boththe highest ranked category and the highest ranked person, a second cellindicates documents which are related to both the second highest rankedcategory and the highest ranked person, a third cell indicates documentswhich are related to both the highest ranked category and the secondhighest ranked person, and a fourth cell indicates documents which arerelated to both the second highest ranked category and the secondhighest ranked person. Links are established from the cells to thedocuments, and the array is displayed. The documents include, but arenot limited to, text files, video clips and sound files.

Other methods, as well as objects, features, benefits and/or advantages,will become apparent upon a review of the following description and thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C illustrate an exemplary method of operation.

FIGS. 2A and 2B illustrate the process of establishing a relationshipbetween categories and a weight for that relationship.

FIG. 3 is an exemplary diagram showing some exemplary categories withtheir exemplary respective weights.

FIG. 4 is an illustration of an exemplary two-dimensional display array.

DETAILED DESCRIPTION OF THE INVENTION

Turning now to the drawing and the several figures therein, theoperation of various embodiments of, and various benefits of, thepresent invention will be discussed.

Establishing Database and Search Formalities

FIGS. 1A-1C illustrate an exemplary method of operation.

In section 101, the database and search formalities are defined.Categories can be added, modified, or deleted at any time. A “category”is, for example, a topic, an issue, an area of interest, etc. In onescenario, some categories might be, for example: war, energy,alternative energy, and green energy. Other categories might be, forexample: business, resources, shipping, taxes, regulations, labor,environment, spills, and carbon footprint. Still other categories mightbe, for example: unemployment, foreclosures, homeless persons, shelter,food, and welfare.

Categories are defined by keywords, and a keyword may be associated withmore than one category. For example, the category “oil” might havekeywords such as oil, crude, well, tanker, and pipeline; the category“Iraq” might have keywords such as oil, progress, etc.

Also, the initial relevance (weight, or strength of association) betweencategories is defined. Categories may be related by any one or more ofseveral factors, discussed below. For convenience, the relationshipbetween two categories is expressed as a number between zero (norelationship) and one (extremely closely related), although therelationship could also be expressed as a number between zero and onehundred, or between any two numbers which adequately define andencompass the desired relationship range. These categories, theirrespective keywords, their relationships, and the strength of theirrelationships are stored, such as in a categories database, table, orindex. Alternatively, such information could be stored in a documentsdatabase, table, or index.

Possible event conditions are also defined. An event condition is astatement of an event, the occurrence of which will change the relevanceof a document. For example, in a political context, event conditions mayinclude a date certain, a date defined by an election, a date defined bya qualifying deadline, etc. Documents relating to candidates before anevent condition occurs may be considered to be relevant, whereasdocuments relating to candidates after that event condition occurs maynot be considered to be relevant except for, perhaps, documents relatedto the winner. Thus, for example, if there are three candidates for aposition, then statements by and articles about all of them may berelevant up to the primary election, at which point at least one of thecandidates will most likely be removed. Therefore, from that point,statements by and articles about that candidate may be deemed asnon-relevant to the search request. Similarly, after the run-offelection, there will presumably be one winner, so statements by andarticles about the two losing candidate may be deemed as non-relevant.The result is that statements by and articles about the winningcandidate will possibly be relevant for future searches but statementsby and articles about the losing candidates will no longer be consideredto be relevant. Preferably, event conditions relating to a document aredefined by an administrator and may be, if desired or necessary, updatedfrom time to time. For example, if the primary election is the eventcondition and the person does not advance, statements by and articlesabout that candidate may be removed according to the event criteria but,if the election results are then overturned and the candidate advancesto the next election, or the candidate is then nominated for a relatedoffice or position, then the administrator can remove or modify theevent condition so that those documents may still be relevant until, ifappropriate, after the next election. Conversely, an event condition maydefine the point at which a document does become relevant. For example,documents regarding election run-off procedures may be deemed asnon-relevant to a search until the primary election is over.

In one embodiment, an event criteria applies to a document across allthe categories with which that document is associated. In anotherembodiment, an event criteria applies to the document only for aspecified category. Thus, if a document is related to one category theevent criteria may specify that it be removed as a relevant document forthat category; but if the document is also related to another category,which does not have event criteria, then the document is retained as arelevant document for that category.

It will be understood that “removed”, “retained”, “discarded”, andsimilar words refer to whether a document is to be further considered inthe search results; they are preferably not removed from the documentdatabase.

Possible search setting limitations are also defined. These allow a userto restrict the search to, for example, in a political context, anoccurrence start date, an occurrence end date, a posted start date, aposted end date, a speech, a town-hall meeting, a debate, anadvertisement, an interview, a candidate for a political office, aholder of a political office, a media document, a special interest groupdocument, a political party document, a user-generated document, a typeof political race, a year of a political race, a country, a state, acounty, a city, a municipality, a regional governmental body, etc. Forexample, if a search setting limitation is a town-hall meeting, thendocuments which do not involve a town hall meeting will be removed fromthe list of initial documents, even if the documents have one or more ofthe search terms or keywords.

The Document Database

In step 105, the documents are migrated into the database. Documents canbe placed into the database at any time, such as when a new documentbecomes available or is uploaded. Although references herein are toplacing in, or migrating a document into, the database, such referencesalso encompass placing a link in the database to the document. Thedocuments include, but are not limited to, text files, video clips andsound files.

Whenever a new document is received and is to be entered into thedocuments database, it is assigned or linked to one or more categories.Thus, each category is associated with, or linked to, or references, oneor more documents. Once a document is received, the information thereinis extracted. For text files, this is a straightforward operation. Fornon-text files, such as video clips or sound files (also called soundbites), the information may be recovered by commercially availablesoftware which converts the audio into a text file. For non-text files,such as scanned or image files, the information may recovered bycommercially available software which performs optical characterrecognition on the file. In one embodiment, the documents include textfiles, video clips, sound bites, scanned files, and image files. Inanother embodiment, the documents are primarily video clips. Also, datamay be added to files, especially for, but not limited to, non-textfiles, which indicates, for example, the date of the document, theprimary speaker, the primary author, the source of the document, otherspeakers or authors, the location where a speech, presentation,demonstration, event or political gathering occurred, etc. All of thisinformation constitutes document data. The documents and document dataare stored, such as in the documents database. Preferably, common wordssuch as “a”, “an”, “the”, “or”, “by”, etc. are not included in thedocument data. Also, preferably but not necessarily, when a searchrequest is received, the document data is searched, rather than theactual document. Of course, the actual document could be searched if ina searchable form. Unless the context requires otherwise, phrases suchas searching the document data, searching the document, and searchingthe document database are generally used interchangeably herein.

The document data is examined for the previously-defined keywords. Thepresence of each keyword in the document data and, possibly but notnecessarily, also the number of times each keyword is used, are storedas part of the document data. The examination of the document data forthe previously-defined keywords also preferably includes examining thedocument data for roots and variations thereof, for example, “buy”encompasses “buys”, “buying”, “bought”, etc. The areas of a documentthat are preferably examined to obtain the document data are the title,abstract, summary, syllabus, and body. Some information may also bepresent in metadata but, as metadata has been the subject of greatcommercial exploitation, especially with respect to web pages, metadatawhich is part of the original document may be ignored, or may beregarded as suspect until verified. Of course, the metadata could alsobe considered without restriction.

In one embodiment, the entity submitting the document for entry into thedatabase may also submit an initial designation as to the categories towhich a document is related. An administrator can change thesedesignations at any time. Further, other category designations may beadded based upon other factors as discussed herein.

In one embodiment, as part of the document data, the categories to whichthe document applies are also stored. In another embodiment, eachcategory also has an index which indicates the documents which arerelevant to (contain one or more keywords of) that category. Eventconditions may also be stored as part of the document data.

The document is preferably also given a score for each category, thescore being dependent upon the number of keywords of a category that arealso in a document; the more keywords for a category that are present,rather than just the number of times that a keyword is present, thehigher the score of the document for that category will be. The scoremay also depend, although preferably to a much lesser degree, or not atall, upon the number of times that a keyword is present in the document.

A typical user is not usually really looking for words but is lookingfor documents which address a specific item of interest. The only methodprovided by the prior art for finding those documents is, however, forthe user to enter specific search words which the user hopes will appearin those documents. Unfortunately, there are often two or more words orphrases which can be used to express the same idea. If the user choosesthe wrong words for the search, that is, words which the author of thedocument did not choose to use, then that document will not be found,however relevant it may be.

For example, if the user is looking for documents which discuss therelationship between a barrel of oil and a gallon of gasoline, the usermay enter the search terms “oil” and “gasoline”. If the most relevantdocument on this subject uses, instead, “sweet crude”, and “refinedpetrochemical products”, then that document will not be found due to thedifferent choice of words used by the author of the document withrespect to the choice of search terms from the searcher.

The search expansion process described herein provides for searchingbeyond merely the user's search terms. Rather, keywords of categorieswhich have a relationship to the user's search terms also become searchterms. This increases the likelihood that documents will be identifiedwhich are relevant to the user's specific item of interest, rather thanlimited to documents identified only by the user's choice of words toexpress the item of interest.

Search Expansion

In step 110 a search request is received from a user, the search requestincluding the user search terms and any limiting criteria. The firststep is to expand the search query beyond the user search terms. Thecategories database is examined to identify categories which have one ormore of the user search terms. These are referred to as “first levelrelated categories”. These categories are related to the user's searchbecause they have, as keywords, one or more of the user search terms.The keywords of these first level related categories are then retrievedpreferably, but not necessarily, excluding keywords which are the sameas the user search terms as those keywords would be duplicative. Theseretrieved keywords, which are now expansion search terms, along with theuser search terms, then constitute the search terms for an expandedsearch query. Now, instead of merely searching using the user's searchterms, the expanded search query is conducted using those user searchterms as well as the retrieved keywords for categories which are relatedto the user's search terms. Thus, the search identifies documentscontaining the user search terms as well as documents which containkeywords from the related categories.

The document data in the documents database is searched using theexpanded query to identify documents (the initial documents) which haveany user search term or any expansion search term. If an initialdocument has event condition criteria associated with it then the eventcondition criteria is applied. Also, any search setting limitationsspecified by the user are applied to the initial documents. Theremaining initial documents are then considered to be relevantdocuments.

Decision 115 then tests whether the search expansion is complete. In oneembodiment, the search expansion is complete if there are at least apredetermined number of documents remaining (the relevant documents)after application of any event condition criteria and any search settinglimitations. This predetermined number may be any desired number but ifthe predetermined number is set too low then the searcher may not beable to see enough documents to satisfy his/her interest and,conversely, if the predetermined number is set too high then thesearcher may be presented with too many documents, which may prompt thesearcher to needlessly, and possibly counterproductively, narrow thesearch. If the search expansion is complete then step 135 is executed.If the search expansion is not complete then step 120 is executed.

Other criteria may also be used to determine whether the searchexpansion is complete. This is discussed below with respect to decisions125 and 130.

In step 120 the next level related categories and search terms areidentified. The categories database is then examined to identifycategories which have one or more of the keywords from the first levelcategories. The keywords corresponding to the user's search terms arepreferably not used because, if any of those keywords were present in acategory, that category would already have been identified as a firstlevel category.

The categories identified by searching for these first level categorykeywords are referred to as “second level related categories”. They arerelated to the user's search because they have, as keywords, one or moreof the keywords used by a category which was more directly related tothe user's search. The remaining keywords of these second level relatedcategories are then retrieved. These retrieved keywords, which becomethe second level expansion keywords, then constitute the search termsfor a further (second level) expanded search query.

The document data in the documents database is then searched using thesesecond level expansion keywords to identify the initial documents whichhave any of those second level expansion search terms. If an initialdocument has event condition criteria associated with it then the eventcondition criteria is applied. Also, any search setting limitationsspecified by the user are applied to the initial documents. Theremaining initial documents from this second level expanded search queryare also considered to be relevant documents.

Decision 125 tests whether the search expansion is complete. If thesearch expansion is complete then step 135 is executed. If the searchexpansion is not complete then decision 130 is executed.

Decision 130 tests whether further expansion is appropriate, that is,whether further expansion is likely to lead to additional relevantdocuments. Decisions 115, 125 and 130 serve to expand the search, and tolimit or terminate the search when a desired result has been achieved orwhen further expansion would be of little benefit, i.e., the search iscomplete. For example, if no new keywords were identified in last passthrough step 120, then further expansion is probably not worthwhile.Also, if no new documents were identified in the last pass through step120, then further expansion is probably not worthwhile. Also, if atleast some predetermined number of related categories has beenidentified then further expansion is not desired. Also, if the remainingcategories are too distantly related to the first level category thenfurther expansion is probably not worthwhile. For example, if thehighest weighed relationship a second level category has to any firstlevel category is 0.7, and if there are two third level categoriesrelated to that second level category, and if one third level categoryhas a weighted relationship of 0.8 to that second level category and theother third level category has a weighted relationship of 0.4 to thatsecond level category, then that one third level category will have aweighted relationship of 0.56 (0.7×0.8) to that first level category andthat other third level category will have a weighted relationship of0.28 (0.7×0.4) to that first level category. If, for example, theminimum acceptable weighted relationship with respect to the firstcategory is 0.5, then the one third level category will be deemed to besufficiently related (0.56) to permit further search expansion by thekeywords in that third level category, but the other third levelcategory will be deemed to be too distantly related (0.28) to warrantfurther search expansion by the keywords in that third level category.

Other minimum acceptable weighted relationship values can be used buttoo high a value may terminate the search expansion too quickly and missrelevant documents, whereas too low a value may result in too manydocuments being produced.

If further expansion is appropriate then a return is made to step 120 toidentify next (third, fourth, etc.) level categories, retrieve theirthird, fourth, etc. level keywords, search the documents database, andapply event condition criteria and search setting limitations to thesethird, fourth, etc., level documents. If further expansion is notappropriate then step 135 is executed.

In addition to the above, the search may also be expanded based uponsynonyms. As mentioned herein, there are often two or more words orphrases which can be used to express the same idea. Therefore, in oneembodiment, there is also a synonyms table. When a search request isreceived, the synonyms table is examined for words and/or phrasescorresponding to the search terms. These synonyms are then used in theexpanded search. For example, synonyms for “war” might be “battle”,“conflict”, “fight”, or “engagement”; and synonyms for “gas” might be“fuel” and “petrol”. Use of synonyms increases the likelihood thatrelevant documents will be found.

In a variation of this embodiment, the table also indicates a weightvalue for each synonym which indicates the degree of relationshipbetween the word and its synonym(s). In the preferred embodiment, thisweight value has a range of 0 to 1. Other ranges can be used, ifdesired. A value of 1 indicates that the words are full synonyms,whereas a value of 0 indicates that the words have little or norelationship as synonyms. This weight may be used to determine therelevance of the document to the original search, such as in conjunctionwith, for example, the document scoring technique described elsewhereherein.

In step 135 the relevant documents from all of the searches are rankedand displayed. It will be recalled that event condition criteria andsearch setting limitations were applied to the initial documents andthus removed certain initial documents, the remaining documents beingthe relevant documents. These relevant documents have initial scoresbased upon the number of user search terms that appear in the document,the number of keywords that appear in the document, and the relevance ofthe category in which the keywords appear in the document. Boosting, orenhancement, factors are then preferably applied to the relevantdocuments to provide final scores for the documents. Categories may alsobecome related categories based on user searches, so step 135 alsopreferably updates the relevance between categories based upon thecurrent user search terms.

At this point it is possible to simply rank the documents by their finalscores and to present the documents as a single column or table ofdocuments to the user.

In another embodiment, however, further processing is performed and thedocuments are presented in an array display which has at least two areasof correlation. In an array presentation the relevant documents areranked according to their scores in a first area and in a second area.These ranked documents are then displayed in a two-dimensional form,with one coordinate vector being the first area and the secondcoordinate vector being the second area. A preferred form of display ofan array is an X-Y matrix; another form of display of an array is acircular display, with the radius being the first area and the anglebeing the second area. Other forms of display are possible andcontemplated, and three-dimensional (for example, X, Y, Z coordinates),and higher displays are also possible and contemplated. Other forms ofdisplay, and three-dimensional and higher displays, however, are harderto present, may be less meaningful or useful to the searcher, and/or mayrequire additional or excessive processing time.

In one embodiment, such as politics, one area or coordinate may bepersons of interest in the political scene, and the other area may betopics of current public interest in the political scene. In anotherembodiment, such as in sports, one area or coordinate may be battingstatistics for major players, and another area or coordinate may bepitching statistics for prominent pitchers. In another embodiment, suchas in finance, one area may be the price of a particular resource, suchas oil, rice, corn syrup, etc., and the other area may be prices ofvarious consumer goods, such as gas, clothes, appliances, etc.

In the above, decision 115 tested whether the expansion was completeand, if not, expansion was performed and decision 125 then testedwhether the expansion was then complete. In another embodiment some, orall, of the related categories are identified before searching thedocuments database. For example, after retrieving the first level searchterms in substep 3 of step 110, it is possible to go directly to step120, identify and retrieve keywords for the next level in substeps 1 and2 of step 120, perform decision 130, and repeat the process untildecision 130 indicates no further expansion. In this manner, all of thekeywords for all of the related categories would have been identifiedbefore searching the documents database. At that point the documentsdatabase would then be searched using the user's search terms and all ofthe identified keywords (as in step 110, substep 4, and step 120,substep 3) to identify the initial documents. The event conditioncriteria and search settings limitations would then be applied to theseinitial documents (as in step 110, substeps 5 and 6, and step 120,substeps 4 and 5) to yield the relevant documents. Step 135 would thenbe performed on those relevant documents.

In another embodiment, each category also has an associated index whichindicates the documents related to that category, as mentioned above.Therefore, when a search request is received, the document data is onlysearched for search terms which are not keywords. These documents, andthe documents listed in the associated index for the identified firstlevel categories, become the initial documents. This procedure may savetime and processing power as compared to searching for each search termand each keyword in each document in the database. For example, bypreprocessing the documents in this manner, if a search request arrivesand all of the search terms are keywords, then there is no need tosearch the documents for the search terms.

In another embodiment, rather than testing whether expansion is completeafter each expansion effort, expansion is conducted until the relevanceof a category to any first level category is below the minimum value, asdiscussed above. Once the appropriate level of search expansion has beendetermined, the documents listed in the indices for the identifiedcategories also become initial documents.

In one embodiment, when documents are presented, the search terms andkeywords in that document are highlighted, or underlined. It will berecalled, however, that documents may be submitted by entities alongwith an initial designation of categories. If this is permitted then,until it has been verified that the document has at least one keywordfor each designated category, it is preferable to search the initialdocuments for the presence of at least one search term or keyword fromthe first level related category and any other level related categories.If the document does not contain a search term or a keyword then thatdocument is deemed to be non-relevant to the search.

In another embodiment, boosting (substep 1 of step 135) is performedbefore applying the event condition criteria and search settingslimitations to these initial documents (as in step 110, substeps 5 and6, and step 120, substeps 4 and 5). This is not preferred, however,because it uses processing time to process a document which may later beremoved.

In another embodiment, substep 6 of step 135 (updating relevance) may beperformed at any point, such as immediately after the first levelrelated categories are identified.

Although the preferred embodiment uses all of the techniques describedabove, the various described features can be used independently andindividually, as desired. For example, the search expansion techniquecould be used to identify relevant documents, which are then simplysorted in a conventional manner and presented to the user in aconventional manner, and without applying event condition criteria orsearch setting limitations, or boosting, or ranking by areas, or usingtwo-dimensional displays. As another example, the event conditioncriteria could be applied to documents identified by a conventionalsearch engine to influence the rank or relevance of the documents. Asanother example, the search settings limitations could be applied todocuments identified by a conventional search engine to influence therank or relevance of the documents. As still another example, theboosting factors could be applied to documents identified by aconventional search engine to influence the rank or relevance of thedocuments. As still another example, ranking by areas, and/or usingtwo-dimensional displays could be applied to documents identified by aconventional search engine to display the documents in a more meaningfulmanner.

Categories

FIGS. 2A and 2B illustrate the process of establishing a relationshipbetween categories and a weight for that relationship. Categories may berelated as a result of any one or more of several factors. For example,if two categories have at least one keyword in common then they arerelated. Also, the more keywords that the categories have in common thenthe more strongly related they are. For convenience, the relationshipbetween two categories is expressed as a number between zero (norelationship) and one (extremely closely related), although therelationship could be expressed as a number between zero and onehundred, or between any two numbers which adequately define andencompass the desired relationship range. These categories, theirrespective keywords, their relationships, and the strength of theirrelationships are stored, such as in the categories database.

In step 205 the categories and their respective keywords are defined, aspreviously mentioned. Decision 210 tests whether two categories(Category (X) And Category (Y)) have any common keywords. If not,decision 220 is executed. If so, then in step 215 the categories aredeemed to be related, and the weight of the relationship between thesetwo categories is determined. The weight of the relationship is basedupon the number of common keywords and the total number of keywords. Forexample, if two categories share one or more common keywords, and haveonly a few differing keywords, then they are strongly related. If,however, two categories share only one common keyword, and have manydiffering keywords, then they are weakly related. Decision 220 is thenexecuted.

If a search request has, for example, two search terms, and one searchterm is a keyword in a first category but not in a second category, andthe other search term is a keyword in the second category but not in thefirst category, and if numerous searchers submit similar searchrequests, so that these two seemingly unrelated categories arerepeatedly both invoked by searches, then there is the presumption thatthe searchers have identified a previously unrecognized relationship, ora new relationship, between the two categories. For example, at onepoint in the past, “Freon” and “ozone layer” might have been unrelatedcategories. Decision 220 therefore tests whether two categories arerepeatedly both encompassed in search requests. If not, decision 230 isexecuted. If so, then in step 225 the categories are deemed to berelated, and the weight of the relationship between these two categoriesis determined. The weight of the relationship is based upon the numberof number of searches that invoke two or more categories which do nothave a common keyword and the total number of searches for thosecategories. The more searches there are which invoke both of thesecategories then the stronger the relationship is between them.

Categories may also be deemed to be related by action of anadministrator. So, even if two categories do not have any commonkeywords, an administrator may decide, and therefore define, that twocategories are related. For example, an administrator may define thecategory “oil” and the category “alternative energy” to be related, evenif they do not have any common keywords. If a relationship is defined byan administrator, then the strength of the relationship is preferablybased on a vote of the administrators. In the preferred environment,there are a plurality of administrators, and any administrator candefine two categories as being related. Preferably, however, a loneadministrator cannot define the weight between the two categories.Rather, each administrator votes on the weight and the votes determinethe weight assigned. In one embodiment, the weight assigned is theaverage value of all of the votes cast. In another embodiment, theweight assigned is the median value of all of the votes cast.

Decision 230 therefore tests whether an administrator has defined twocategories as being related. If not, decision 240 is executed. If so,then in step 235 the categories are deemed to be related, and the weightof the relationship between these two categories is determined. Thendecision 240 is executed.

Two categories may also be related if they reference the same documentor documents. The more documents they have in common, the stronger therelationship between the categories, even if they do not have anykeywords in common. Decision 240 therefore tests whether the twocategories reference the same document. If not, then decision 250 isexecuted. If so, then in step 245 the categories are deemed to berelated, and the weight of the relationship between these two categoriesis determined. The weight is dependent upon the number of commondocuments and the total number of documents that each categoryreferences.

Decision 250 tests whether the relationships between all categories havebeen considered or updated. If not, a return to step 210 is made and therelationship between two more categories are considered. If so, then instep 255 the process is ended.

The decisions and processes above are preferably performed repeatedly,such as on a predetermined schedule, and/or whenever a qualifying searchoccurs, and/or when an administrator inputs a suggestion that twocategories are related, and/or when a new category is defined, and/orwhen the keywords for a category is updated, and/or after apredetermined number of new documents have been entered. Also, theparticular order of the decisions and steps is not critical so, forexample, the administrator decision process could be performed beforethe qualifying search process. In one embodiment, once the possibleweights have been determined, the highest weight is deemed to be theappropriate weight. In another embodiment, the average weight, or themedian weight, is deemed to be the appropriate weight.

FIG. 3 is an exemplary diagram showing some exemplary categories withtheir respective exemplary weights.

Boosting Factors

After the initial scores for the identified documents have beendetermined, and after the event criteria and search setting limitationshave been applied, boosting factors are applied to these remaining,relevant documents so that more relevant documents will have higherscores. In the preferred embodiment, the following boosting factors areused: a Lucene Score; a “Document Rating” boosting coefficient; a“Document Type” boosting coefficient; a “Document Source” boostingcoefficient; a “Number of Views” boosting coefficient, and a “Person ofInterest” boosting coefficient. It will be appreciated that none, one,some, or all of these factors may be used.

The Lucene Score is the score that is returned by a Lucene searchengine. The Lucene Score is normalized and takes a value between 0and 1. The score of query “q” for a document “d” correlates to thecosine-distance or dot-product between document and query vectors in aVector Space Model (VSM) of Information Retrieval. A document whosevector is closer to the query vector in that model is scored higher.Some of the factors used in computing the Lucene score are the frequencyof the term (the number of times the term appears in the currentdocument), the inverse of the number of documents in the database inwhich the term appears, how many of the search terms are found in thespecified document, and a normalizing factor used to make scores betweenqueries comparable. As a result, more occurrences of a given term resultin a higher score, rarer terms result in a higher contribution to thetotal score, and a document that contains more of the query's terms willreceive a higher score than another document with fewer query terms. Thenormalizing factor does not affect document ranking as all rankeddocuments are preferably multiplied by the same factor, but this makesscores from different queries (or even different indexes) comparable.Additional information on Lucene scoring is available athttp://lucene.apache.org.

In a preferred embodiment, searchers who have viewed the document areallowed to rate the relevance of the document to the search query. The“Document Rating” boosting coefficient is a normalized average rating onthe document by those searchers and, preferably but not necessarily, hasa value between 0 and 1.

The “Document Type” boosting coefficient is defined for every DocumentType value, is also preferably a normalized coefficient, and alsopreferably has a value between 0 and 1. This coefficient gives a highervalue to documents arising out of certain settings than of othersettings. In a preferred embodiment, and in a political context, forexample, the preferred boosting coefficients are: Speech—1; Town-HallMeeting—0.7; Debate—0.5; Advertisement—0.3; and Interview—0.3. Thesevalues are preferred, but are exemplary, and other values may be useddepending upon the emphasis desired. Also, other document types will beappropriate for other areas, such as in a sports context (e.g.,championship game, playoff game, regular season game, exhibition game,etc.) or in a business context (e.g., Securities Exchange Commission(SEC) filing, annual report, quarterly report, public statement,advertisement, etc.).

The “Document Source” boosting coefficient is defined for every DocumentSource value, is also preferably a normalized coefficient, and alsopreferably has a value between 0 and 1. This coefficient gives differentvalues to documents based upon the source of the document. For example,a statement by a candidate is given a higher value than a report on thestatement by a media group or a special interest group. In a preferredembodiment, and in a political context, for example, the preferredboosting coefficients are: Candidates or Political Office Holders—1;Media—0.7; Special Interest Groups (SIGs)—0.5; political parties—0.3;and from a general user—0.3. Also, other document source types will beappropriate for other areas, such as in a sports context (e.g., league,team coach, player, etc.) or in a business context (e.g., owner, CEO,Board of Directors, CFO, president, vice-president, manager, employee,etc.).

The “Number of Views” boosting coefficient is also preferably anormalized coefficient, and also preferably has a value betweenapproximately 0 and 1. This coefficient is calculated as:1−(1/(ln(Number of Views+3))), where “ln” is the natural logarithm,“Number of Views” is the number of times that the document has beenviewed, and the number “3” is an approximation, used for convenience, ofthe value for “e”−2.71828 . . . .

The “Person of Interest” boosting coefficient is a coefficient that iscalculated for the primary speaker (or author) in the document. Thiscoefficient acknowledges that documents regarding certain people, andcertain offices, are more likely to be relevant than documents regardingother people. For example, the President of the United States isconsidered to be a person of greater interest than, for example, themayor of a city. Preferably, this coefficient is not a normalizedcoefficient and, preferably, may have a value greater than 1. If adocument has several primary speakers (or authors) then the maximum“Person of Interest” boosting coefficient is applied. Also, in apolitical context, for example, the weight of the relationship between aPerson and a Political Office are relevant: a person can be the currentholder of a political office—1; the person can be a former holder of thepolitical office—0.75; or the person can be a candidate for thepolitical office—1. It is possible for more than one of theserelationships to be present at the same time. For example, the personcould be a former mayor, who was subsequently elected and is now thecurrent mayor, and who is also running for re-election for mayor. Also,other Person of Interest types will be appropriate for other areas, suchas in a sports context or in a business context.

In a preferred embodiment, in a preferred context, different politicaloffices are deemed to be in different bands or levels and therefore tohave different weights. Table 1 shows exemplary, and preferred,political offices, bands, and weights.

TABLE 1 BANDS, WEIGHTS, AND POLITICAL OFFICES Political Office, Band/Appointment, Or Other Equivalent Political Level Relevant PositionWeight Office Positions 1 President 25 2 Vice President 23 3Presidential Press Secretary 22 4 Presidential Cabinet Member 20Secretary of Defense, Secretary of State, Secretary of The Interior,Attorney General, etc. 5 Other Presidential 18 FBI Director, Chief ofAdministration and Officials Staff, EPA Director, Trade Rep,Ambassadors, Federal Reserve Chair, etc. 6 Foreign Heads of State and 17British Prime Minister, Ambassadors Canadian Prime Minister, RussianPresident 7 United States Senator 16 8 United States Representative 15 9Other Major National Political 14 The First Figure or Advisor, or amajor Lady/Gentleman, Media figure Reverend Jesse Jackson, DemocraticNational Committee Chair; Republican National Committee Chair, Judicial(e.g., Supreme Court and Federal Judges) 10 Military Commanders 13 ArmyGenerals, Navy Admirals, Air Force Generals 11 State Governor 12 12State Lieutenant Governor 10 13 Other Major State Political 9 FirstLady/Gentleman Figure or a State Media Figure of the State, well- knownactivists and religious speakers, Judicial 14 State Senator 8 15 StateRepresentative 7 16 State Department Secretary, 6 Attorney Generals,Commissioner or Officer Secretary of State, Secretary of Transportation,etc. 17 City Mayor 5 18 City Councilperson 3 19 County Commissioner 2 20Other office 1

To determine the Person of Interest boosting coefficient the relationsbetween the person and the political office are determined and, for eachrelated political office of the person, the band/level of the person isdetermined based on the related political office, and is then multipliedby the weight of the relationship between the person and the politicaloffice (current, former, candidate) to produce a temporary value. Theweight of the Person is then the maximum of these temporary values.Also, if several relationships have this same maximum weight then therelationship with the highest band level is selected for that person.

For example, if a person is a former holder of the ‘President’ PoliticalOffice then one temporary weight of that person is W1=25*0.75=18.75. Ifthat person is also the current holder of the “Air Force General”Political Office then another temporary weight for that person isW2=13*1=13. If that person is also a candidate for the “AttorneyGeneral” position then another temporary weight for that person isW3=20*1=20. That person therefore has a current weight of 20 and isband/level 4.

If that person is not appointed to be the Attorney General then thatperson will then have a weight of 18.75 (former President) and aband/level of 1.

A person's weight and band/level is re-determined any time therelationship of the person to a political office is changed.

Thus, the initial score of a document is then multiplied by one or moreof, and preferably all of, the factors described above to determine afinal score for that document: the Lucene Score; the Document Ratingboosting coefficient; the Document Type boosting coefficient; theDocument Source boosting coefficient; the Number of Views boostingcoefficient, and the Person of Interest boosting coefficient.

In one embodiment, the “documents” are video clips.

Ranking Categories and Persons

When ranking the relevance of categories and persons, several factorsare considered, including, but not limited to: the number of searchesfor that category/person compared to the total number of category/personsearches, the number of searches made for related categories/persons,the number of documents which are related to the category, the totalnumber of defined categories, the person's position (candidate, inoffice, formerly in office), the person's past/present office, and/orthe proximity of the current date to an election date (either before orafter).

A category is assigned a score according to the following:

${{Issue}\mspace{14mu}{Rank}\mspace{14mu}\underset{{- i} = 1}{\overset{``}{SCORE}}\frac{S_{X}}{{NoS}_{i}}} + {k\; 2*\frac{\sum\limits_{I = 1}^{N}\; W_{IX}}{\sum\limits_{i = 1}^{N}\;{\sum\limits_{j = 1}^{N}\; W_{ij}}}} + {k\; 3*\frac{\sum\limits_{I = 1}^{N}\;{W_{IX}*{NoS}_{I}}}{\sum\limits_{j = 1}^{N}\;{\sum\limits_{i = 1}^{N}\;{W_{ij}*{NoS}_{i}}}}} + {k\; 4*{NoV}_{x}} + {k\; 5*\frac{\sum\limits_{i = 1}^{N}\;{W_{ix}*{NoV}_{i}}}{\sum\limits_{j = 1}^{N}\;{\sum\limits_{i = 1}^{N}\;{W_{ij}*{NoV}_{i}}}}}$

wherein:

X denotes a category;

k1 is a coefficient representing the number of searches made regardingcategory X;

NoS_(x) is the number of searches made regarding category X;

N is the total number of defined categories;

NoS_(i) is the number of searches made for each category I;

k2 is a coefficient representing the weight of the relationship betweencategory X and all other categories;

W_(ix) is the weighted relationship between category X and anothercategory I;

k3 is a coefficient representing the weighted number of searches madeagainst related categories;

k4 is a coefficient representing the number of documents related tocategory X;

NoV_(x) is the number of documents related to category X; and

k5 is a coefficient representing the number of documents related to thecategory related to category X.

This scoring technique can be used regardless of the type of thedocument but, in a preferred embodiment, the documents are primarilyvideo clips. Once the scores have been assigned to the categories thenthe categories can be sorted or ranked based upon those scores. Notethat the score, and therefore the rank, of a category will change fromtime to time.

This score can also be assigned to a document related to that category.If a document is related to several categories then the document isassigned the highest score of the related categories. Documents can thenbe ranked based upon those assigned scores.

Similarly, a Person of Interest (or any person) can also be assigned ascore according to the following:

${SCORE} = {{{kp}*{BoI}_{y}} + {{kn}*\frac{{NoS}_{y}}{\sum\limits_{i = 1}^{M}\;{NoS}_{i}}} + {{kd}*{{PoE}({DateOfElection})}}}$

wherein

Y denotes a person;

k_(p) is a coefficient for the broadness of impact rank;

Bol_(y) is the broadness of impact rank of the person Y, the values inTable 1 may be used for this factor, preferably modified by the weight(current, former, candidate) of person;

kn is a coefficient for the number of searches made regarding the personY;

NoS_(y) is the number of searches made regarding the person Y;

NoS_(i) is the number of searchers made regarding each person;

kd is a coefficient for the proximity of an election involving theperson Y; and

PoE(DateOfElection) is a relevance factor based upon the proximity of anelection involving the person Y;

${{PoE}({DateOfElection})} = \left\{ \begin{matrix}\begin{matrix}{\frac{k_{{PoE}\; 1}}{{DateOfElection} - {CurrentDate}},} \\{{{CurrentDate} < {DateOfElection}},{{in}\mspace{14mu}{months}\mspace{14mu} 1},} \\{{CurrentDate} = {DateOfElection}}\end{matrix} \\\begin{matrix}{\frac{k_{{PoE}\; 2}}{{CurrentDate} - {DateOfElection}},} \\{{{CurrentDate} > {DateOfElection}},{{in}\mspace{14mu}{days}}}\end{matrix}\end{matrix} \right.$

wherein:

k_(poE1) is a coefficient for proximity of elections for the time beforethe date of the election involving the person Y;

k_(poE2) is a coefficient for proximity of elections for the time afterthe date of the election involving the person Y.

Once the scores have been assigned to the persons then the persons canbe sorted or ranked based upon those scores. Note that the score, andtherefore the rank, of a person will change from time to time.

This score can also be assigned to a document related to that category.If a document is related to several persons then the document isassigned the highest score of the related persons. Documents can then beranked based upon those assigned scores.

Displays

Ranking categories and persons allows for other display options. In oneembodiment, after the search request has been entered, the user may bepresented with various display options. For example, based upon thesearch request, various categories may have been identified and/orvarious persons of interest may have been identified. Therefore, theuser may be presented with a pull-down menu of display options, forexample: the search results are displayed in a conventional singlecolumn format; the categories are displayed and the most relevantdocuments within each categories are presented; the persons of interestare displayed and the most relevant documents with respect to eachperson of interest are presented; or the display is a two dimensionaldisplay, the categories are and the persons of interest are the twocoordinate axes, and the most relevant document or documents withrespect to both a category and a person of interest are presented.

FIG. 4 shows such an exemplary two-dimensional array 400. One coordinateaxis is the “CATEGORY” axis 405, and the other coordinate axis is the“PERSON” (person of interest) axis 410. Each cell 415CxPy (e.g.,415C1P1, 415C1P2, 415C2P1, etc., where “Cx” is the category and “Py” isthe person) indicates one or more of the documents (e.g., D1, D2, D3,etc.) which are ranked as more relevant to both that particular categoryand that particular person. “D#” indicates a reference to a document,which may a link, the title of the document, part of the headline of thedocument, a keyword in the document, or some other desired informationabout that document; preferably information which will advise the userof the content of that document. The number of categories, the number ofpersons, and the number of documents referenced in the table, and theparticular information displayed about each document in the table, isdetermined, for the most part, by how large (screen size) the table isdesired to be. Thus, a table which is to be viewed as a single screenwill have a limited number of categories, a limited number of persons, alimited number of documents referenced, and/or a limited amount ofinformation which is displayed. Conversely, a table which is spreadacross several screens, so that the user has to scroll left/right and/orup/down to see the entire table, will have a larger number ofcategories, a larger number of persons, a larger number of documentsreferenced, and/or a greater amount of information which is displayed.Preferably, the reference “D#” is a hyperlink so clicking on thereference will bring up the document itself, or at least a part of thedocument or some information about the document.

In another embodiment, rather than presenting two or more documents ineach cell of an array, only one document is presented, such as theheadline or a summary of document. Clicking on the document indicationwill cause more, or all, of the document, or some more information aboutthe document, to be brought up and presented.

In one embodiment, the system tracks search requests in differentcontext areas, such as, for example, politics, sports, business, etc. Anarray is then generated for that context area which, based upon thesearch requests, indicates the categories of interest, the persons ofinterest, and the related documents. Thus, a user may go to the systemweb site and be immediately presented with an array for a predeterminedcontext area, such as politics, which may change from time to time, orthere may be several arrays, such as politics, business, sports, etc.,and the user may be presented with the choice of which area the userdesires to see. The user can click on the desired choice and bepresented with an array for the desired context. Of course, the useralso has the option of conducting a search rather than just viewing theexisting arrays.

In another embodiment, entities which pay for the privilege may beallowed to submit search requests, and have the results presented in anarray as a form of paid advertising. The array may be presented eitheron the system web site, for one fee, or via a hyperlink from the website of the entity, for another fee. For example, a political party maywish to present its candidate(s) in a favorable light. That party wouldthen submit one or more search requests which contained search termsrelated to the desired persons or categories. When that party had foundthe combination of search terms, categories, and persons that gave thedesired result, then that result would be saved and displayed, such asin an array, at either the system web site or via a hyperlink from theparty's web site.

In another embodiment, categories of current interest and persons ofcurrent interest are identified and ranked. The categories and personsof interest may be determined by keeping statistics on recent searchrequests by users and/or by a vote of the administrators. Thesecategories and persons of current interest, along with their respectiverankings are used for the coordinate axes for a display, and therelevant documents are indicated in the display.

When a person goes to the web site then, either as the first web page,or as a web page which can be brought up by clicking on a link, thearray display of the categories and persons of current interest andrelevant documents is presented. Thus, the person can immediately seedocuments of current interest without conducting a search. This mayinspire the person to conduct a search to obtain more information on oneor more of the categories or persons indicated.

In another embodiment, if a person is viewing a document, for example,an article in an online newspaper or a news reporting web site, or if aperson is listening to video clip or a sound bite, and that persondesires more information, then clicking on, for example, the title orthe headline of the document, or a syllabus or summary of the document,or the first paragraph of the document, or anywhere within the document,or even a “search” icon associated with the document, then at least someof the information in the document (for example, the title, headline, orthe summary, etc.) is sent to the search engine.

In one embodiment, this causes the search engine to conduct a search,preferably an expanded search, using the terms from that information,and the search results are then presented to the user. In anotherembodiment, as each document is preferably associated with one or morecategories, clicking on the document causes a search to be initiated asif the user had entered the keywords of those categories. In anotherembodiment, this causes search engine to conduct a search, preferably anexpanded search, using the terms from that information.

The result of clicking the document may be that other relevant documentsare presented to the user. In another embodiment, the most relevantvideo found by the resulting search is presented to the user. This videomay be shown via a standard movie player or a custom movie player. Thisvideo may be a full screen video or may be a window in the screen. Theremay or may not be a charge for viewing the video although, preferably,the user would be allowed to see, without charge, enough of the video todetermine whether the video is something that the user wanted to seemore of.

Document Sources, Control and Review

Documents to be entered into the documents database may come fromseveral different sources. For example, the system administrators maysearch for and identify documents; a robot, such as a web crawler, maysearch the Internet for documents containing any of the keywords in thecategories database; the administrators may cause the system to“subscribe” to emails or newsfeeds from selected persons or entities;entities paying for the privilege may be allowed to upload documents forentry; entities paying for the privilege may be allowed to uploaddocuments for entry and propose the document categories; theadministrators may allow the system to accept document submissions fromselected person or entities; and/or anyone accessing the system, such asvisiting the web site, can upload documents. Combinations of the abovecan also be used. In one embodiment, any document to be entered has tobe approved for entry by at least one administrator. In anotherembodiment, documents submitted are automatically entered, subject tolater removal or restriction by one or more administrators.

Also, as mentioned above, whenever a user views a document, the user mayenter a rating which indicates how relevant the user thought thedocument was to the user's search request. Thus, if a document has beenrated by users as being of little relevance to the search then one ormore administrators may review the document and the search requests todetermine whether the document is in one or more incorrect categories,or whether the document includes keywords which are not relevant to thedocument but have been inserted simply to make the document show up insearch results for more exposure.

In one embodiment, a plurality of administrators control the overalloperation, preferably by at least a majority vote. The administratorsare preferably selected based upon their knowledge and experience in aparticular category or categories. For example, some administrators fora political category may be political science professors, newscommentators, political analysts (preferably independent), certain typesof talk show or talk radio hosts, etc. As another example, someadministrators for a sports category may be team coaches, sportscommentators, sports writers, former major players, etc. “Superadministrators” may also be appointed or elected to resolve disputeswhich occur between administrators and/or to break a tie vote.

Any process descriptions, steps, or blocks in the figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the preferredembodiments of the systems and methods described herein in which stepsor functions may be deleted, executed out of order from that shown ordiscussed, executed concurrently, substantially concurrently, orsequentially, or in reverse order, depending on the functionalityinvolved.

A system for implementing the present invention preferably includes oneor more computers, such as servers having associated databases,operating system software, software suitable for conducting searches,input and output ports and/or portals, Internet access, static, dynamic,and redundant memories, security features, etc. Except for the varioustechniques described herein, such components, and the operation andarrangement and interconnection thereof, are well known in the field ofsearch engines and systems. It is not believed that a block diagramshowing these well-known components is necessary or would impart anyadditional information to one of skill in the art and, therefore, such ablock diagram is not included herein.

Conditional language, such as, among others, “can”, “could”, “might”, or“may”, unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments optionally could include, while some other embodiments donot include, certain features, elements and/or steps. Thus, suchconditional language indicates, in general, that those features,elements and/or step are not required for every implementation orembodiment. Also, “such as”, “for example”, and the like are intended toindicate some, but not all, possibilities, and are not intended to belimiting or to limit the possibilities to those stated.

Although various embodiments of the present invention have beendescribed in detail herein, other variations may occur to those readingthis disclosure without departing from the spirit of the presentinvention. Further, various aspects, benefits, capabilities, embodimentsand/or features described herein may be used independently or incombination, as appropriate to achieve a desired result; it is notnecessary to incorporate every aspect, benefit, capability, embodimentand/or feature into a single implementation in order to obtain specificdesired aspects, benefits, capabilities, and/or features, and all suchvariations are included within the scope of the present inventions.Accordingly, the scope of the present invention is to be defined orlimited only by the accompanying claims.

What is claimed is:
 1. A computer-implemented method of ranking persons,each person being associated with at least one document, a search engineidentifying relevant documents based upon a search request for a person,the computer-implemented method comprising: assigning a score to eachperson using the relationship${{People}\mspace{14mu}{Rank}\mspace{14mu}(Y)} = {{{kp}*{BoI}_{y}} + {{kn}*\frac{{NoS}_{y}}{\sum\limits_{i = 1}^{M}\;{NoS}_{i}}} + {{kd}*{{PoE}({DateOfElection})}}}$wherein: Y denotes a person; kp is a coefficient for a broadness ofimpact rank; Bol_(y) is the broadness of impact rank of the person Y; knis a coefficient for a number of searches made regarding the person Y;NoS_(y) is the number of searches made regarding the person Y; NoS_(i)is a number of searches made regarding each person; kd is a coefficientfor a proximity of an election involving the person Y;PoE(DateOfElection) is a relevance factor based upon the proximity of anelection involving the person Y;${{PoE}({DateOfElection})} = \left\{ \begin{matrix}\begin{matrix}{\frac{k_{{PoE}\; 1}}{{DateOfElection} - {CurrentDate}},} \\{{{CurrentDate} < {DateOfElection}},{{in}\mspace{14mu}{months}\mspace{14mu} 1},} \\{{CurrentDate} = {DateOfElection}}\end{matrix} \\\begin{matrix}{\frac{k_{{PoE}\; 2}}{{CurrentDate} - {DateOfElection}},} \\{{{CurrentDate} > {DateOfElection}},{{in}\mspace{14mu}{days}}}\end{matrix}\end{matrix} \right.$ kpoE1 is a coefficient for a proximity ofelections for a time before a date of the election involving the personY; kpoE2 is a coefficient for a proximity of elections for a time afterthe date of the election involving the person Y; and, ranking thepersons based upon the assigned scores, wherein the assigning of a scoreto each person and the ranking of persons based upon the assigned scoresare performed using software running on at least one computer includingat least one non-transitory computer storage medium for storing thesoftware for performing the assigning and the ranking, and wherein ahighest score of the persons relevant to the document is assigned to adocument and documents are ranked based upon the assigned scores.
 2. Themethod of claim 1 wherein the coefficient for the broadness of impactrank is a function of a type of a political office.
 3. The method ofclaim 2 wherein the broadness of impact rank of the person Y isdetermined at least in part by whether the person Y is either a currentholder of the political office, a former holder of the political office,or a candidate for the political office.